Cognitive architectures of causal learning

Funding: Agence Nationale de la Recherche ANR - PRC, Jan 2019 - Dec 2023

Consortium: Andrea Brovelli (PI), Mateus Joffily (GATE, Lyon), Mehdi Khamassi (ISIR, Paris), Julien Bastin (GIN, Grenoble)

The aim of this project is to study the neural and computational bases of human goal-directed causal learning by combining human neurophysiology (MEG and intracranial SEEG) and neuroimaging (fMRI) techniques with computational models of learning (Reinforcement Learning and Active Inference).

Past Projects


Model-free and model-based inference and validation workflows for causal brain network discovery

Funding: Human Brain Project HBP/EBRAINS, Oct 2020 - Sept 2023

Consortium: Andrea Brovelli (PI), Jean Daunizeau (ICM, Paris), Gustavo Deco (UPF, Barcelona), Stefano Panzeri (IIT, Italy), Petra Ritter (Charité, Berlin), Olivier David (GIN, Grenoble)

The project aims at combining data-driven and model-based approaches for the inference of brain causal connectivity and the validation of whole-brain models. The goal is to integrate workflows and methods into EBRAINS. This projet is part of WP1 of SGA3 of the HBP.


Modelling information dynamics

Funding: 2 yrs post-doc fellowship from the Institut de Convergence ILCB, May 2019 - April 2021

Consortium: Andrea Brovelli (co-PI), Demian Battaglia (co-PI; INS, Marseille; USIAS, Strasbourg)

The project aims at developping an multi-area interacting model based on the ring model. The goal is to study neural computations such as stimulus encoding, information transfer and information modification by means of simulation studies. This projet is part of Institut de Convergence ILCB.


The influence of directional interactions in brain networks in predicting cognitive deficits post-stroke

Funding: FLAG-ERA HBP Partnering project, Jan 2018 - dec 2020

Consortium: Andrea Brovelli (co-PI), Marizio Corbetta (PI; Univ Padova, Italy)

The project aims to develop and test methods for the analysis of directional interactions among brain regions in resting state and task-evoked fMRI data in healthy controls and stroke damaged individuals, and to develop a computational model of an injured brain that replicates both the empirically measured patterns of connectivity abnormalities and behavioral deficits.